SA609
SA609 <- readRDS("data/SA609.Rdata")
SA000 <- readRDS("data/SA000.Rdata")
SA001 <- readRDS("data/SA001.Rdata")
cells_SA609 <- data.table::fread("metadata/fitness_material/tables/fitness_cell_assignment_feb07_2020.tsv") %>%
as_tibble() %>%
select(-V1) %>%
dplyr::rename(clone_id = letters) %>%
dplyr::rename(cell_id = single_cell_id) %>%
filter(str_detect(datatag, "SA609$|SA000|SA001"))
haps <- bind_rows(SA609$haplotypes, SA000$haplotypes, SA001$haplotypes) %>%
filter(cell_id %in% cells_SA609$cell_id)
CN <- bind_rows(SA609$CN, SA000$CN, SA001$CN) %>%
filter(cell_id %in% cells_SA609$cell_id)
cnbaf <- combineBAFCN(haps, CN, phasing = "distribution")
ascn <- callAlleleSpecificCNHMM(cnbaf, ncores = 20)
library(ape)
source("R/tree_utils.R")
cells_SA609 <- data.table::fread("metadata/fitness_material/tables/fitness_cell_assignment_feb07_2020.tsv") %>%
as_tibble() %>%
select(-V1) %>%
dplyr::rename(clone_id = letters) %>%
dplyr::rename(cell_id = single_cell_id) %>%
filter(str_detect(datatag, "SA609$"))
ascn_SA609 <- filter(ascn, cell_id %in% cells_SA609$cell_id)
tree <- read.tree("metadata/fitness_material/trees/subset/SA609_only.newick")
tree <- removeleafloci(tree)
clonesSA609 <- cell_order_from_tree(tree, cells_SA609,
cells = unique(ascn_SA609$cell_id))
cols <- fread("metadata/fitness_material/tables/clone_colours.csv") %>%
as_tibble() %>%
filter(str_detect(datatag, "SA609$")) %>%
distinct(colour, clone_id)
clone_pal <- c(cols$colour, "Black")
names(clone_pal) <- c(cols$clone_id, "Un")
heatmap_SA609_1 <- plotHeatmap(ascn_SA609,
clusters = clonesSA609,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
clone_pal = clone_pal,
plotcol = "state")
heatmap_SA609_2 <- plotHeatmap(ascn_SA609,
clusters = clonesSA609,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
clone_pal = clone_pal,
plotcol = "state_phase")
Heatmaps
CN state
heatmap_SA609_1

ASCN state
heatmap_SA609_2

BAF plots per clone
CNclones <- ascn_SA609 %>%
left_join(., select(cells_SA609, clone_id, cell_id)) %>%
group_by(chr, start, end, clone_id) %>%
summarise(state = schnapps::Mode(state),
state_phase = schnapps::Mode(state_phase),
state_min = schnapps::Mode(state_min),
BAF = median(BAF),
copy = median(copy)) %>%
ungroup() %>%
mutate(cell_id = paste0("Clone ", clone_id))
for (cl in unique(CNclones$cell_id)){
cat('###', cl,' \n')
print(plotCNprofileBAF(CNclones, cellid = cl))
cat(' \n \n')
}
Clone A

Clone B

Clone C

Clone D

Clone E

Clone F

Clone G

Clone H

Clone Un

write_csv(CNclones %>% dplyr:: select(-cell_id), "results/BAFperclone/SA609.csv")
SA000
library(ape)
source("R/tree_utils.R")
cells_SA000 <- data.table::fread("metadata/fitness_material/tables/fitness_cell_assignment_feb07_2020.tsv") %>%
as_tibble() %>%
select(-V1) %>%
dplyr::rename(clone_id = letters) %>%
dplyr::rename(cell_id = single_cell_id) %>%
filter(str_detect(datatag, "SA000$"))
ascn_SA000 <- filter(ascn, cell_id %in% cells_SA000$cell_id)
tree <- read.tree("metadata/fitness_material/trees/SA000_tree.newick")
tree <- removeleafloci(tree)
clonesSA000 <- cell_order_from_tree(tree, cells_SA000,
cells = unique(ascn_SA000$cell_id))
cols <- fread("metadata/fitness_material/tables/clone_colours.csv") %>%
as_tibble() %>%
filter(str_detect(datatag, "SA000$")) %>%
distinct(colour, clone_id)
clone_pal <- c(cols$colour, "Black")
names(clone_pal) <- c(cols$clone_id, "Un")
heatmap_SA000_1 <- plotHeatmap(ascn_SA000,
clusters = clonesSA000,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
clone_pal = clone_pal,
plotcol = "state")
heatmap_SA000_2 <- plotHeatmap(ascn_SA000,
clusters = clonesSA000,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
clone_pal = clone_pal,
plotcol = "state_phase")
Heatmaps
CN state
heatmap_SA000_1

ASCN state
heatmap_SA000_2

BAF plots per clone
CNclones <- ascn_SA000 %>%
left_join(., select(cells_SA000, clone_id, cell_id)) %>%
group_by(chr, start, end, clone_id) %>%
summarise(state = schnapps::Mode(state),
state_phase = schnapps::Mode(state_phase),
state_min = schnapps::Mode(state_min),
BAF = median(BAF),
copy = median(copy)) %>%
ungroup() %>%
mutate(cell_id = paste0("Clone ", clone_id))
for (cl in unique(CNclones$cell_id)){
cat('###', cl,' \n')
print(plotCNprofileBAF(CNclones, cellid = cl))
cat(' \n \n')
}
Clone A

Clone B

write_csv(CNclones %>% dplyr:: select(-cell_id), "results/BAFperclone/SA000.csv")
SA001
library(ape)
source("R/tree_utils.R")
cells_SA001 <- data.table::fread("metadata/fitness_material/tables/fitness_cell_assignment_feb07_2020.tsv") %>%
as_tibble() %>%
select(-V1) %>%
dplyr::rename(clone_id = letters) %>%
dplyr::rename(cell_id = single_cell_id) %>%
filter(str_detect(datatag, "SA001$"))
ascn_SA001 <- filter(ascn, cell_id %in% cells_SA001$cell_id)
#tree <- read.tree("metadata/fitness_material/trees/SA000_tree.newick")
#tree <- removeleafloci(tree)
# clonesSA001 <- cell_order_from_tree(tree, cells_SA001,
# cells = unique(ascn_SA001$cell_id))
clonesSA001 <- cells_SA001 %>% filter(cell_id %in% unique(ascn_SA001$cell_id))
cols <- fread("metadata/fitness_material/tables/clone_colours.csv") %>%
as_tibble() %>%
filter(str_detect(datatag, "SA001$")) %>%
distinct(colour, clone_id)
clone_pal <- c(cols$colour, "Black")
names(clone_pal) <- c(cols$clone_id, "Un")
heatmap_SA001_1 <- plotHeatmap(ascn_SA001,
clusters = clonesSA001,
plottree = F,
reorderclusters = T,
spacer_cols = 15,
clone_pal = clone_pal,
plotcol = "state")
heatmap_SA001_2 <- plotHeatmap(ascn_SA001,
clusters = clonesSA001,
plottree = F,
reorderclusters = T,
spacer_cols = 15,
clone_pal = clone_pal,
plotcol = "state_phase")
Heatmaps
CN state
heatmap_SA001_1

ASCN state
heatmap_SA001_2

BAF plots per clone
CNclones <- ascn_SA001 %>%
left_join(., select(cells_SA001, clone_id, cell_id)) %>%
group_by(chr, start, end, clone_id) %>%
summarise(state = schnapps::Mode(state),
state_phase = schnapps::Mode(state_phase),
state_min = schnapps::Mode(state_min),
BAF = median(BAF),
copy = median(copy)) %>%
ungroup() %>%
mutate(cell_id = paste0("Clone ", clone_id))
for (cl in unique(CNclones$cell_id)){
cat('###', cl,' \n')
print(plotCNprofileBAF(CNclones, cellid = cl))
cat(' \n \n')
}
Clone A

Clone B

write_csv(CNclones %>% dplyr:: select(-cell_id), "results/BAFperclone/SA001.csv")
SA532
SA532 <- readRDS("data/SA532.Rdata")
cells_SA532 <- data.table::fread("metadata/fitness_material/tables/fitness_cell_assignment_feb07_2020.tsv") %>%
as_tibble() %>%
select(-V1) %>%
dplyr::rename(clone_id = letters) %>%
dplyr::rename(cell_id = single_cell_id) %>%
filter(str_detect(datatag, "SA532"))
haps <- SA532$haplotypes %>%
filter(cell_id %in% cells_SA532$cell_id)
CN <- SA532$CN %>%
filter(cell_id %in% cells_SA532$cell_id)
cnbaf <- combineBAFCN(haps, CN, phasing = "distribution")
ascn <- callAlleleSpecificCNHMM(cnbaf, ncores = 20)
library(ape)
source("R/tree_utils.R")
tree <- read.tree("metadata/fitness_material/trees/SA532_tree.newick")
tree <- removeleafloci(tree)
clonesSA532 <- cell_order_from_tree(tree, cells_SA532,
cells = unique(ascn$cell_id))
cols <- fread("metadata/fitness_material/tables/clone_colours.csv") %>%
as_tibble() %>%
filter(str_detect(datatag, "SA532$")) %>%
distinct(colour, clone_id)
clone_pal <- c(cols$colour, "Black")
names(clone_pal) <- c(cols$clone_id, "Un")
heatmap_SA532_1 <- plotHeatmap(ascn,
clusters = clonesSA532,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
plotcol = "state",
clone_pal = clone_pal)
heatmap_SA532_2 <- plotHeatmap(ascn,
clusters = clonesSA532,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
plotcol = "state_phase",
clone_pal = clone_pal)
Heatmaps
CN state
heatmap_SA532_1

ASCN state
heatmap_SA532_2

BAF plots per clone
CNclones <- ascn %>%
left_join(., select(cells_SA532, clone_id, cell_id)) %>%
group_by(chr, start, end, clone_id) %>%
summarise(state = schnapps::Mode(state),
state_phase = schnapps::Mode(state_phase),
state_min = schnapps::Mode(state_min),
BAF = median(BAF),
copy = median(copy)) %>%
ungroup() %>%
mutate(cell_id = paste0("Clone ", clone_id))
for (cl in unique(CNclones$cell_id)){
cat('###', cl,' \n')
print(plotCNprofileBAF(CNclones, cellid = cl))
cat(' \n \n')
}
Clone A

Clone B

Clone C

Clone D

Clone Un

write_csv(CNclones %>% dplyr:: select(-cell_id), "results/BAFperclone/SA532.csv")
SA906a
SA906a <- readRDS("data/SA906a.Rdata")
SA906b <- readRDS("data/SA906b.Rdata")
#cnbaf <- combineBAFCN(bind_rows(SA906a$haplotypes, SA906b$haplotypes), bind_rows(SA906a$CN, SA906b$CN), phasing = "x")
cnbaf <- combineBAFCN(bind_rows(SA906a$haplotypes, SA906b$haplotypes), bind_rows(SA906a$CN, SA906b$CN))
ascn <- callAlleleSpecificCNHMM(cnbaf, ncores = 12)
SA906atimepoint <- read_csv("metadata/fitness_other/SA906atimepoint.txt")
SA906btimepoint <- read_csv("metadata/fitness_other/SA906btimepoint.txt")
cells <- data.table::fread("metadata/fitness_material/tables/fitness_cell_assignment_feb07_2020.tsv") %>%
as_tibble() %>%
select(-V1) %>%
mutate(cell_id = str_replace(single_cell_id, "SA906.-", "SA906-")) %>%
dplyr::rename(clone_id = letters) %>%
filter(str_detect(datatag, "SA906"))
SA906a$ASCN <- ascn %>%
filter(cell_id %in% (filter(cells, datatag == "SA906a") %>% pull(cell_id)))
SA906b$ASCN <- ascn %>%
filter(cell_id %in% (filter(cells, datatag == "SA906b") %>% pull(cell_id)))
library(ape)
source("R/tree_utils.R")
tree <- read.tree("metadata/fitness_material/trees/SA906a_tree.newick")
tree <- removeleafloci(tree)
clonesSA906a <- cell_order_from_tree(tree, cells %>% filter(datatag == "SA906a") %>%
mutate(cell_id = str_replace(cell_id, "SA906", "SA906a")),
cells = str_replace(unique(SA906a$ASCN$cell_id), "SA906", "SA906a"))
cols <- fread("metadata/fitness_material/tables/clone_colours.csv") %>%
as_tibble() %>%
filter(str_detect(datatag, "SA906a$")) %>%
distinct(colour, clone_id)
clone_pal <- c(cols$colour, "Black")
names(clone_pal) <- c(cols$clone_id, "Un")
heatmap_SA906a_1 <- plotHeatmap(SA906a$ASCN %>% mutate(cell_id = str_replace(cell_id, "SA906", "SA906a")),
clusters = clonesSA906a,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
plotcol = "state",
clone_pal = clone_pal)
heatmap_SA906a_2 <- plotHeatmap(SA906a$ASCN %>% mutate(cell_id = str_replace(cell_id, "SA906", "SA906a")),
clusters = clonesSA906a,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
plotcol = "state_phase",
clone_pal = clone_pal)
Heatmaps
CN state
heatmap_SA906a_1

ASCN state
heatmap_SA906a_2

BAF plots per clone
CNclones <- SA906a$ASCN %>%
left_join(., select(cells, clone_id, cell_id)) %>%
group_by(chr, start, end, clone_id) %>%
summarise(state = schnapps::Mode(state),
state_phase = schnapps::Mode(state_phase),
state_min = schnapps::Mode(state_min),
BAF = median(BAF),
copy = median(copy)) %>%
ungroup() %>%
mutate(cell_id = paste0("Clone ", clone_id))
for (cl in unique(CNclones$cell_id)){
cat('###', cl,' \n')
print(plotCNprofileBAF(CNclones, cellid = cl))
cat(' \n \n')
}
Clone A

Clone B

Clone C

Clone D

Clone E

Clone F

Clone G

Clone H

Clone I

Clone J

Clone K

Clone Un

write_csv(CNclones %>% dplyr:: select(-cell_id), "results/BAFperclone/SA906a.csv")
SA906b
library(ape)
source("R/tree_utils.R")
tree <- read.tree("metadata/fitness_material/trees/SA906b_tree.newick")
tree <- removeleafloci(tree)
clonesSA906b <- cell_order_from_tree(tree, cells %>% filter(datatag == "SA906b") %>%
mutate(cell_id = str_replace(cell_id, "SA906", "SA906b")),
cells = str_replace(unique(SA906b$ASCN$cell_id), "SA906", "SA906b"))
cols <- fread("metadata/fitness_material/tables/clone_colours.csv") %>%
as_tibble() %>%
filter(str_detect(datatag, "SA906b$")) %>%
distinct(colour, clone_id)
clone_pal <- c(cols$colour, "Black")
names(clone_pal) <- c(cols$clone_id, "Un")
heatmap_SA906b_1 <- plotHeatmap(SA906b$ASCN %>% mutate(cell_id = str_replace(cell_id, "SA906", "SA906b")),
clusters = clonesSA906b,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
plotcol = "state",
clone_pal = clone_pal)
heatmap_SA906b_2 <- plotHeatmap(SA906b$ASCN %>% mutate(cell_id = str_replace(cell_id, "SA906", "SA906b")),
clusters = clonesSA906b,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
plotcol = "state_phase",
clone_pal = clone_pal)
Heatmaps
CN state
heatmap_SA906b_1

ASCN state
heatmap_SA906b_2

BAF plots per clone
CNclones <- SA906b$ASCN %>%
left_join(., select(cells, clone_id, cell_id)) %>%
group_by(chr, start, end, clone_id) %>%
summarise(state = schnapps::Mode(state),
state_phase = schnapps::Mode(state_phase),
state_min = schnapps::Mode(state_min),
BAF = median(BAF),
copy = median(copy)) %>%
ungroup() %>%
mutate(cell_id = paste0("Clone ", clone_id))
for (cl in unique(CNclones$cell_id)){
cat('###', cl,' \n')
print(plotCNprofileBAF(CNclones, cellid = cl))
cat(' \n \n')
}
Clone A

Clone B

Clone C

Clone D

Clone E

Clone F

Clone G

Clone H

Clone I

Clone Un

write_csv(CNclones %>% dplyr:: select(-cell_id), "results/BAFperclone/SA906b.csv")
SA039
SA039 <- readRDS("data/SA039.Rdata")
cells_SA039 <- data.table::fread("metadata/fitness_material/tables/fitness_cell_assignment_feb07_2020.tsv") %>%
as_tibble() %>%
select(-V1) %>%
dplyr::rename(clone_id = letters) %>%
dplyr::rename(cell_id = single_cell_id) %>%
filter(str_detect(datatag, "SA039"))
CN <- SA039$CN %>%
filter(cell_id %in% cells_SA039$cell_id)
ascn <- CN
library(ape)
source("R/tree_utils.R")
tree <- read.tree("metadata/fitness_material/trees/SA039_tree.newick")
tree <- removeleafloci(tree)
clonesSA039 <- cell_order_from_tree(tree, cells_SA039,
cells = unique(ascn$cell_id))
cols <- fread("metadata/fitness_material/tables/clone_colours.csv") %>%
as_tibble() %>%
filter(str_detect(datatag, "SA039$")) %>%
distinct(colour, clone_id)
clone_pal <- c(cols$colour, "Black")
names(clone_pal) <- c(cols$clone_id, "Un")
heatmap_SA039_1 <- plotHeatmap(ascn,
clusters = clonesSA039,
plottree = F,
reorderclusters = F,
spacer_cols = 15,
plotcol = "state",
clone_pal = clone_pal)
Heatmaps
CN state
heatmap_SA039_1

BAF plots per clone
CNclones <- ascn %>%
left_join(., select(cells_SA039, clone_id, cell_id)) %>%
group_by(chr, start, end, clone_id) %>%
summarise(state = schnapps::Mode(state),
copy = median(copy)) %>%
ungroup() %>%
mutate(cell_id = paste0("Clone ", clone_id))
for (cl in unique(CNclones$cell_id)){
cat('###', cl,' \n')
print(plotCNprofile(CNclones, cellid = cl))
cat(' \n \n')
}
Clone A

Clone B

Clone C

Clone D

Clone E

Clone F

write_csv(CNclones %>% dplyr:: select(-cell_id), "results/BAFperclone/SA039.csv")